From 1b427d95fef12433ec633605fae0cc57a205d332 Mon Sep 17 00:00:00 2001 From: Fabian Pedregosa Date: Wed, 29 Jun 2011 09:54:33 +0200 Subject: [PATCH] Move scipy_future into utils.arpack --- scikits/learn/manifold/locally_linear.py | 4 ++-- scikits/learn/utils/__init__.py | 1 - scikits/learn/{manifold/scipy_future.py => utils/arpack.py} | 0 3 files changed, 2 insertions(+), 3 deletions(-) rename scikits/learn/{manifold/scipy_future.py => utils/arpack.py} (100%) diff --git a/scikits/learn/manifold/locally_linear.py b/scikits/learn/manifold/locally_linear.py index 6fe7f9ffdf18b..3b030981c92ca 100644 --- a/scikits/learn/manifold/locally_linear.py +++ b/scikits/learn/manifold/locally_linear.py @@ -7,9 +7,9 @@ from scipy.linalg import eigh, svd, qr from scipy.sparse import linalg, eye, csr_matrix from scipy.sparse.linalg import LinearOperator -from scipy_future import eigsh from ..base import BaseEstimator from ..utils import check_random_state +from ..utils.arpack import eigsh from ..neighbors import kneighbors_graph, BallTree, barycenter_weights try: @@ -315,7 +315,7 @@ def locally_linear_embedding( # >> W_hat = np.zeros( (N,s_i) ) # >> W_hat[neighbors[i],:] = Wi # >> W_hat[i] -= 1 - # >> M += np.dot(W_hat,W_hat.T) + # >> M += np.dot(W_hat,W_hat.T) #We can do this much more efficiently: nbrs_x, nbrs_y = np.meshgrid(neighbors[i], neighbors[i]) M[nbrs_x, nbrs_y] += np.dot(Wi, Wi.T) diff --git a/scikits/learn/utils/__init__.py b/scikits/learn/utils/__init__.py index 05617739503b2..3234322ab1c28 100644 --- a/scikits/learn/utils/__init__.py +++ b/scikits/learn/utils/__init__.py @@ -1,7 +1,6 @@ import numpy as np import scipy.sparse as sp - def safe_asanyarray(X, dtype=None, order=None): if sp.issparse(X): return X diff --git a/scikits/learn/manifold/scipy_future.py b/scikits/learn/utils/arpack.py similarity index 100% rename from scikits/learn/manifold/scipy_future.py rename to scikits/learn/utils/arpack.py